where \(z\)  is a latent variable assumed to follow a multivariate Gaussian distribution with a diagonal covariance matrix. Due to intractability of true posterior, we approximate the posterior distribution of \(z\)  through a recognition neural network \(q_{\psi }(z|\mathcal {P}, Y)\) . The prior distribution of \(z\)  given \(\mathrm {Y}\)  is modelled through a prior network \(p_{\theta }(z|Y)\) . For more details about CVAE, we refer to [1]}.
